Key findings - matched to report

Authors

Jolyon Miles-Wilson

Celestin Okoroji

Published

March 25, 2025

1 Ethnicity categorisations

For reference, the table below provides a disambiguation of how ethnicities have been grouped in this analysis.

For analyses using the disaggregated (survey) categories, the reference category is “English / Welsh / Scottish / Northern Irish / British”.

For analyses using the aggregated categories, the reference category is “White British”

Ethnicity: Survey Ethnicity: Aggregated Ethnicity: Binary
English / Welsh / Scottish / Northern Irish / British White British White British
Irish White other Non-White British
Gypsy or Irish Traveller White other Non-White British
Roma White other Non-White British
Any other White background White other Non-White British
White and Black Caribbean Mixed/Multiple ethnic group Non-White British
White and Black African Mixed/Multiple ethnic group Non-White British
White and Asian Mixed/Multiple ethnic group Non-White British
Any other Mixed / Multiple ethnic background Mixed/Multiple ethnic group Non-White British
Indian Asian/Asian British Non-White British
Pakistani Asian/Asian British Non-White British
Bangladeshi Asian/Asian British Non-White British
Chinese Asian/Asian British Non-White British
Any other Asian background Asian/Asian British Non-White British
African Black/African/Caribbean/Black British Non-White British
Caribbean Black/African/Caribbean/Black British Non-White British
Any other Black, Black British, or Caribbean background Black/African/Caribbean/Black British Non-White British
Arab Arab/British Arab Non-White British
Any other ethnic group Other ethnic group Non-White British
Don’t think of myself as any of these Prefer not to say Non-White British
Prefer not to say Prefer not to say Non-White British
NA Prefer not to say Non-White British

2 Chapter 2: How many outsourced workers are there in the UK?

2.1 How many UK workers are outsourced?

#how-many
  • Around 1 in 6 UK workers meet our definition of an outsourced worker
  • The ‘outsourced sub-group’ is the most dominant of the three sub-groups - meaning the total group is predominantly made up of people who self-identify as an outsourced worker and they say they are hired to do work that is long-term or ongoing. People included in this sub-group (either uniquely, or while also meeting the criteria for at least one of the other sub-groups) make up around 67% (check) of our total outsourced group, or nearly 7 in 10. This group makes up X of all UK workers.

1 in 6 (17%) of UK workers are outsourced.1

In terms of the the different possible types of outsourced groups2, the numbers are as follows:

  1. Definitely outsourced: 11%
  2. Likely agency: 3%
  3. High indicators: 3%

People included in this sub-group (either uniquely, or while also meeting the criteria for at least one of the other sub-groups) make up around 68% of our total outsourced group. This group makes up 11% of all UK workers.

#non-exclusive-subgroups1
  • The two other sub-groups – the agency and indicators sub-groups – are less dominant in comparison. Around 58% of all respondents meet the criteria for either or both of these sub-groups, but this falls to around 33% if we exclude people who are already captured in the outsourced sub-group. Excluding the first sub-group, these other two groups makes up X of all UK workers.

The percentages here refer to the number of people who are outsourced (super-ordinate group), not the total number of respondents. Below I provide percentages as function of the outsourced super-ordinate group as well as the total sample

Group criteria

  • Outsourced, defined as responding ‘I am sure I am outsourced’ or ‘I might be outsourced’, and responding ‘I do work on a long-term basis’.
  • Likely agency, defined as those responding ‘I am sure I am agency’ and ‘I do work on a long-term basis’, excluding those people who are already defined as being outsourced.
  • High indicators: defined as responding TRUE to 5 or 6 of the outsourcing indicators, as well as responding ‘I do work on a long-term basis’, excluding those people who are already defined as outsourced or likely agency.
Including outsourced group
agency_or_indicator freq n total perc N
agency 342.6956 344 10155 3.374649 10155
both 106.3656 116 10155 1.047421 10155
indicator 513.2645 516 10155 5.054303 10155
neither 9192.6744 9179 10155 90.523627 10155
Exluding outsourced group
agency_or_indicator freq n total perc N
agency 231.43068 231 8993.922 2.5731897 9032
both 35.10624 38 8993.922 0.3903329 9032
indicator 280.74106 291 8993.922 3.1214531 9032
neither 8446.64421 8472 8993.922 93.9150243 9032

9.48% of the whole sample meet the criteria for either or both of these sub-groups. This falls to 6.08% if we exclude people who are already captured in the outsourced sub-group.

Out of those who are in the ‘outsourced’ status (i.e., the combination of the three outsourced groups), 57.99% meet the criteria for either or both of these sub-groups, but this falls to around 33.27% if we exclude people who are already captured in the outsourced sub-group.

#non-exclusive-subgroups2
  • There is some overlap between these sub-groups, but they are not like for like. Just over a quarter (27%) of respondents are in more than one sub-group, while nearly three quarters (73%) of respondents are uniquely captured in just one of the three sub-groups.

Just over a quarter (26.35%) of respondents are in more than one sub-group, while nearly three quarters (73.65%) of respondents are uniquely captured in just one of the three sub-groups.3

2.2 Evaluating our total estimate

#evaluating-total-estimate To do
  • Around 1 in 4 “outsourced” respondents sit in more than one sub-group within our definition, but around 3 in 4 are uniquely captured in just one of the three sub-groups - predominantly in the outsourced sub-group.
  • As figure X shows, not all respondents in the outsourced sub-group said yes five or six of our six outsourcing

3 Chapter 3: Who are the UK’s outsourced workers?

3.1 Demographic breakdown

Demographic variables:

  • Categorical
  • Numeric

We want them broken down by

  • outsourcing status
    • high low pay
  • outsourcing group
    • high low pay

3.1.1 Ethnicity by outsourcing status

3.1.1.1 Collapsed ethnicity4

Ethnicity by outsourcing status (%)
outsourcing_status White British Arab/British Arab Asian/Asian British Black/African/Caribbean/Black British Mixed/Multiple ethnic group Other ethnic group Prefer not to say White other
Not outsourced 78.01 0.24 7.46 2.83 1.65 0.28 3.63 5.89
Outsourced 66.91 0.70 12.68 5.67 2.64 0.41 4.33 6.65

3.1.1.2 Full ethnicity5

Ethnicity by outsourcing status (%)
outsourcing_status English / Welsh / Scottish / Northern Irish / British Irish Gypsy or Irish Traveller Roma Any other White background White and Black Caribbean White and Black African White and Asian Any other Mixed / Multiple ethnic background Indian Pakistani Bangladeshi Chinese Any other Asian background African Caribbean Any other Black, Black British, or Caribbean background Arab Any other ethnic group Don’t think of myself as any of these Prefer not to say NA
Not outsourced 78.01 1.17 0.10 0.06 4.56 0.63 0.24 0.38 0.40 2.81 1.14 0.63 1.47 1.40 1.86 0.67 0.30 0.24 0.28 0.07 0.28 3.28
Outsourced 66.91 0.87 0.14 0.13 5.50 0.32 0.86 0.53 0.93 4.33 3.15 1.35 1.23 2.62 4.10 0.88 0.69 0.70 0.41 0.17 0.40 3.75

3.1.1.3 By high/low pay

3.1.1.3.1 Collapsed ethnicity6
Ethnicity by outsourcing status and income group(%)
outsourcing_status income_group White British Arab/British Arab Asian/Asian British Black/African/Caribbean/Black British Mixed/Multiple ethnic group Other ethnic group Prefer not to say White other
Not outsourced Not low 78.53 0.27 7.96 2.91 1.61 0.39 2.28 6.05
Not outsourced Low 80.18 0.26 6.33 3.33 1.59 NA 2.37 5.94
Outsourced Not low 64.95 0.63 15.03 6.85 2.13 0.33 2.99 7.10
Outsourced Low 68.67 0.40 10.64 3.61 4.53 0.91 4.61 6.64
3.1.1.3.2 Full ethnicity7
Ethnicity by outsourcing status and income group(%)
outsourcing_status income_group English / Welsh / Scottish / Northern Irish / British Irish Gypsy or Irish Traveller Roma Any other White background White and Black Caribbean White and Black African White and Asian Any other Mixed / Multiple ethnic background Indian Pakistani Bangladeshi Chinese Any other Asian background African Caribbean Any other Black, Black British, or Caribbean background Arab Any other ethnic group Don’t think of myself as any of these Prefer not to say NA
Not outsourced Not low 78.53 0.97 0.13 0.10 4.84 0.53 0.26 0.42 0.40 3.14 1.14 0.50 1.78 1.40 1.97 0.70 0.24 0.27 0.39 0.06 0.22 1.99
Not outsourced Low 80.18 1.38 0.04 NA 4.52 0.63 0.17 0.36 0.43 2.03 1.02 0.55 0.85 1.88 2.19 0.66 0.48 0.26 NA 0.04 0.26 2.07
Outsourced Not low 64.95 0.79 0.26 0.24 5.81 0.22 1.03 0.24 0.64 5.77 3.12 1.37 1.60 3.17 5.04 1.02 0.80 0.63 0.33 0.22 0.23 2.54
Outsourced Low 68.67 0.57 NA NA 6.07 0.70 0.77 1.20 1.85 3.13 3.98 1.41 0.87 1.26 2.64 0.83 0.14 0.40 0.91 0.09 0.59 3.92

3.1.2 Ethnicity by oustourcing group

3.1.2.1 Collapsed ethnicity8

Ethnicity by outsourcing group (%)
outsourcing_group White British Arab/British Arab Asian/Asian British Black/African/Caribbean/Black British Mixed/Multiple ethnic group Other ethnic group Prefer not to say White other
Not outsourced 78.01 0.24 7.46 2.83 1.65 0.28 3.63 5.89
Outsourced 67.02 0.43 11.86 5.78 2.64 0.13 4.62 7.53
Likely agency 65.41 1.28 12.80 6.52 3.18 1.48 3.80 5.54
High indicators 67.88 1.29 15.99 4.42 2.17 0.57 3.64 4.04

3.1.2.2 Full ethnicity9

Ethnicity by outsourcing group (%)
outsourcing_group English / Welsh / Scottish / Northern Irish / British Irish Gypsy or Irish Traveller Roma Any other White background White and Black Caribbean White and Black African White and Asian Any other Mixed / Multiple ethnic background Indian Pakistani Bangladeshi Chinese Any other Asian background African Caribbean Any other Black, Black British, or Caribbean background Arab Any other ethnic group Don’t think of myself as any of these Prefer not to say NA
Not outsourced 78.01 1.17 0.10 0.06 4.56 0.63 0.24 0.38 0.40 2.81 1.14 0.63 1.47 1.40 1.86 0.67 0.30 0.24 0.28 0.07 0.28 3.28
Outsourced 67.02 0.97 0.21 0.13 6.22 0.33 0.95 0.50 0.85 3.79 2.82 1.55 1.10 2.61 4.07 0.90 0.81 0.43 0.13 0.22 0.14 4.25
Likely agency 65.41 0.25 NA 0.29 5.00 0.26 0.34 0.95 1.63 4.44 3.65 0.98 0.59 3.13 4.81 1.27 0.44 1.28 1.48 0.15 0.20 3.45
High indicators 67.88 1.06 NA NA 2.98 0.35 0.93 0.28 0.61 6.48 4.07 0.88 2.39 2.17 3.54 0.47 0.41 1.29 0.57 NA 1.68 1.96

3.1.2.3 By high/low pay

3.1.2.3.1 Collapsed ethnicity10
Ethnicity by outsourcing status and income group(%)
outsourcing_group income_group White British Arab/British Arab Asian/Asian British Black/African/Caribbean/Black British Mixed/Multiple ethnic group Other ethnic group Prefer not to say White other
Not outsourced Not low 78.53 0.27 7.96 2.91 1.61 0.39 2.28 6.05
Not outsourced Low 80.18 0.26 6.33 3.33 1.59 NA 2.37 5.94
Outsourced Not low 63.23 0.52 14.55 7.26 2.06 0.25 3.40 8.73
Outsourced Low 70.91 0.55 9.52 3.32 4.16 NA 4.52 7.02
Likely agency Not low 67.09 0.79 12.62 8.39 2.14 NA 3.26 5.71
Likely agency Low 61.13 NA 14.97 2.34 7.15 5.48 2.33 6.60
High indicators Not low 68.81 0.87 18.63 4.20 2.36 0.86 1.39 2.87
High indicators Low 64.73 NA 11.73 7.72 2.86 NA 8.96 3.99
3.1.2.3.2 Full ethnicity11
Ethnicity by outsourcing status and income group(%)
outsourcing_group income_group English / Welsh / Scottish / Northern Irish / British Irish Gypsy or Irish Traveller Roma Any other White background White and Black Caribbean White and Black African White and Asian Any other Mixed / Multiple ethnic background Indian Pakistani Bangladeshi Chinese Any other Asian background African Caribbean Any other Black, Black British, or Caribbean background Arab Any other ethnic group Don’t think of myself as any of these Prefer not to say NA
Not outsourced Not low 78.53 0.97 0.13 0.10 4.84 0.53 0.26 0.42 0.40 3.14 1.14 0.50 1.78 1.40 1.97 0.70 0.24 0.27 0.39 0.06 0.22 1.99
Not outsourced Low 80.18 1.38 0.04 NA 4.52 0.63 0.17 0.36 0.43 2.03 1.02 0.55 0.85 1.88 2.19 0.66 0.48 0.26 NA 0.04 0.26 2.07
Outsourced Not low 63.23 1.04 0.40 0.24 7.05 0.15 1.03 0.30 0.57 5.22 3.25 1.30 1.40 3.39 5.02 1.29 0.96 0.52 0.25 0.27 0.27 2.85
Outsourced Low 70.91 0.55 NA NA 6.46 0.81 1.05 0.74 1.57 2.46 3.16 1.92 0.69 1.29 2.53 0.79 NA 0.55 NA 0.12 NA 4.39
Likely agency Not low 67.09 0.42 NA 0.49 4.80 0.44 0.58 0.28 0.83 5.21 3.55 1.66 NA 2.19 7.32 0.33 0.74 0.79 NA 0.25 0.33 2.68
Likely agency Low 61.13 NA NA NA 6.60 NA NA 2.93 4.22 5.08 5.80 NA 2.21 1.89 0.85 1.49 NA NA 5.48 NA NA 2.33
High indicators Not low 68.81 0.29 NA NA 2.58 0.26 1.41 NA 0.68 8.06 2.33 1.34 3.61 3.29 3.20 0.71 0.30 0.87 0.86 NA NA 1.39
High indicators Low 64.73 1.63 NA NA 2.36 1.09 NA 1.77 NA 4.79 6.93 NA NA NA 6.35 NA 1.37 NA NA NA 5.84 3.12

3.1.3 Gender by outsourcing status12

Gender by outsourcing status (%)
outsourcing_status Female Male Other Prefer not to say
Not outsourced 51.89 47.28 0.15 0.68
Outsourced 43.00 56.40 0.15 0.44

3.1.3.1 By high/low pay13

Gender by outsourcing status and income group(%)
outsourcing_status income_group Female Male Other Prefer not to say
Not outsourced Not low 46.31 53.32 0.13 0.24
Not outsourced Low 71.70 27.83 0.18 0.29
Outsourced Not low 35.85 63.83 0.14 0.18
Outsourced Low 60.34 38.98 0.30 0.38

3.1.4 Gender by outsourcing group14

Gender by outsourcing group (%)
outsourcing_group Female Male Other Prefer not to say
Not outsourced 51.89 47.28 0.15 0.68
Outsourced 45.34 53.94 0.23 0.50
Likely agency 43.02 56.66 NA 0.32
High indicators 33.34 66.35 NA 0.32

3.1.4.1 By high/low pay15

Gender by outsourcing group and income group(%)
outsourcing_group income_group Female Male Other Prefer not to say
Not outsourced Not low 46.31 53.32 0.13 0.24
Not outsourced Low 71.70 27.83 0.18 0.29
Outsourced Not low 38.22 61.42 0.21 0.15
Outsourced Low 62.10 36.97 0.41 0.52
Likely agency Not low 37.89 61.57 NA 0.54
Likely agency Low 53.35 46.65 NA NA
High indicators Not low 26.27 73.73 NA NA
High indicators Low 58.97 41.03 NA NA

3.3 Outsourced workers are on average younger than non-outsourced workers

#age
  • We find that outsourced workers are significantly younger than non-outsourced workers, on average. The median age of an outsourced worker is 35, compared to a median age of 43 for a non-outsourced worker.
  •  the outsourced and indicator sub-groups – people who directly said that they were or might be outsourced, or ticked a high number of our indicators of outsourced working – see higher proportions of younger workers than the “agency” sub-group.
#age-violin

INSERT VIOLIN PLOT CHART HERE SHOWING MEDIAN AGE OF EACH SUB-GROUP, COMPARED TO NON-OUTSOURCED WORKERS. Is this necessary? We already have the density plots

Outsourced workers are on average younger than non-outsourced workers. The median age of the outsourced group is 36 , compared to 43 for the not outsourced group.26 This difference is statistically significant.27

Outsourcing group Mean Median Min Max Standard dev. N
Not outsourced 42.80 43 16 80 13.08 8472
Outsourced 38.63 36 16 78 13.07 1683

The higher concentration of younger workers identified above appears to be driven primarily by the ‘outsourced’ and ‘high indicator’ groups, whilst the ‘likely agency’ group follows a similar pattern to the non-outsourced group.28

Outsourcing status Income group Mean Median Min Max Standard dev. N
Not outsourced Not low 41.97 41 18 78 12.47 5280
Not outsourced Low 42.87 43 16 80 15.09 1644
Outsourced Not low 37.96 35 18 77 12.53 986
Outsourced Low 39.05 37 16 78 14.06 381

Outsourcing group Mean Median Min Max Standard dev. N
Not outsourced 42.80 43 16 80 13.08 8472
Outsourced 38.40 35 16 78 13.09 1123
Likely agency 39.80 38 18 77 13.49 269
High indicators 38.49 35 18 72 12.55 291

Outsourcing group Income group Mean Median Min Max Standard dev. N
Not outsourced Not low 41.97 41.00 18 78.0 12.47 5280
Not outsourced Low 42.87 43.00 16 80.0 15.09 1644
Outsourced Not low 37.81 34.52 18 67.0 12.57 625
Outsourced Low 39.07 37.00 16 78.0 13.89 272
Likely agency Not low 39.33 38.00 18 77.0 12.66 168
Likely agency Low 39.35 37.00 19 71.5 15.66 63
High indicators Not low 37.29 35.00 18 65.0 12.25 193
High indicators Low 38.42 34.59 19 67.0 12.82 46

#gender
  • The evidence also finds meaningful differences by gender between the outsourced and non-outsourced groups in our data. Men make up 56% of the outsourced workforce compared to 47% of the non-outsourced workforce, a nearly 10 percentage point difference.

  • Outsourced workers are 1.44 times more likely to be male than female. 

  • The group with the largest proportion of men in the workforce is the ‘high indicators’ group (66.35%), followed by the ‘likely agency’ group (56.66%), followed by the ‘outsourced’ group (53.94%). Comparison of outsourced and non-outsourced workers finds that

  • Someone in the high indicators sub-group is 2.18 times more likely to be male than female.

  • Someone in the agency sub-group is 1.45 times more likely to be male than female.

  • Someone in the outsourced sub-group is 1.31 times more likely to be male than female.

#gender-sector
  • Possible addition: Will readers want to know more about how this intersects with the roles or sectors with higher rates of outsourcing – even if this is just an interpretive comment from us on how gender interacts with jobs and sectors more generally in the labour market?
# weights:  12 (6 variable)
initial  value 14077.819237 
iter  10 value 7610.573378
iter  20 value 7465.550476
final  value 7465.517316 
converged

The outsourced workforce consists of a greater proportion of males than the non-outsourced workforce.29 Men make up 56% of the outsourced workforce compared to 47% of the non-outsourced workforce. This difference is statistically significant; outsourced workers, compared to non-outsourced workers, are 1.44 times more likely to be male than female.30

# weights:  20 (12 variable)
initial  value 14077.819237 
iter  10 value 7977.307669
iter  20 value 7461.899083
iter  30 value 7457.852026
iter  40 value 7457.374598
final  value 7457.362521 
converged

Breaking down by outsourcing group, we find that the group with the largest proportion of men in the workforce is the ‘high indicators’ group (66.35%), followed by the ‘likely agency’ group (56.66%), followed by the ‘outsourced’ group (53.94%). Statistically speaking, compared to a not outsourced person,

  • Someone in the high indicators group is 2.18 times more likely to be male than female.
  • Someone in the likely agency group is 1.45 times more likely tobe male than female.
  • Someone in the outsourced group is 1.31 times more likely tobe male than female.

Additionally, people identifying as ‘Other’ gender are absent from the high indicators and likely agency groups, though given the small N (14) for this group, this finding is unlikely to be meaningful.

3.4 Outsourced workers are more likely to work in some sectors than others; but seem to be spread across the labour market

#sectors
  • The three most common sectors for outsourced workers in our survey to be employed within – excluding those with an N size below X (50?) – were administrative and support service activities; water supply, sewerage, waste supply and remediation activities; and other service activities

  • Five of the twenty employment sectors have at least 1 in 5 of their workforce “outsourced”: more than the average of around 17% across the whole workforce.

Here we explore what proportion of workers in each sector are outsourced.31

The plot below shows the proportion of outsourced and not outsourced workers within each sector. I.e. this is showing what sectors have higher and lower proportions of outsourced workers.

The top three Sectors with the highest proportion of outsourced workers are:

  • ACTIVITIES OF HOUSEHOLDS AS EMPLOYERS; UNDIFFERENTIATED GOODS-AND SERVICES-PRODUCING ACTIVITIES OF HOUSEHOLDS FOR OWN US (note that N = 31)
  • ADMINISTRATIVE AND SUPPORT SERVICE ACTIVITIES
  • WATER SUPPLY; SEWERAGE, WASTE MANAGEMENT AND REMEDIATION ACTIVITIES

Note that for an undefined sector (‘Not found’) contained one of the largest proportions of outsourced workers (31% of workers in the ‘Not found’ category were outsourced).

A key takeaway here is that whereas the total outsourced population is 17%, this figure varies by sector, from 0% for Mining… and Extraterritoral organisations… all the way to 36% for Activities of households as employers, with 5 out 20 sectors having at least 20% of their workforce outsourced.

#sectors-ogroup
  • Figure X also shows how the total outsourced group in each sector splits into our three outsourced “sub-groups”. We find – as you might expect, based on its dominance within the group of outsourced workers – that outsourced workers in every sector are most likely to be in the “outsourced sub-group”, i.e. those who self-identified as outsourced workers.

4 Pay

’#pay
  • Using regression analysis, we find that outsourced workers are on average paid £2170 less than non-outsourced workers .

  • The “outsourced sub-group” earns £3,813 less, and the “agency sub-group” £2,603 less, than the non-outsourced group. This finds that pay is lowest in the “outsourced sub-group” of workers, i.e. those who directly identified themselves as being outsourced. Figure X below shows the median and distribution of pay across the three outsourced sub-groups and the non-outsourced group, for comparison.

#pay-violin

Violin plot for the above

The tables and plots below show descriptive statistics on income and its distribution for outsourced and non-outsourced people. Regression analysis shows that outsourced workers are on average paid £2170 less annually than non-outsourced workers.32 Per week, outsourced workers are on average paid £47 less than non-outsourced workers

Weekly stats here33

Outsourcing status n Mean Median Min Max Standard dev.
Not outsourced 6924 26781.29 25120.67 2000 66250 13365.63
Outsourced 1367 24611.38 23061.99 2400 66108 12998.56

Outsourcing status n Mean Median Min Max Standard dev.
Not outsourced 6924 575.41 539.73 42.97 1423.42 287.17
Outsourced 1367 528.79 495.50 51.57 1420.37 279.28

The tables and plots below show descriptive statistics on income and its distribution for outsrouced groups. Only the full outsourced subgroup has lower income than non-outsourced people. Regression analysis shows that outsourced workers are on average paid £3100 less annually than non-outsourced workers.34 Per week, outsourced workers are on average paid £67 less than non-outsourced workers

Weekly stats here35

Outsourcing group n Mean Median Min Max Standard dev.
Not outsourced 6924 26781.29 25120.67 2000.0 66250.00 13365.63
Outsourced 897 23680.86 22165.73 2400.0 66000.00 12783.87
Likely agency 231 25081.11 22800.00 3194.7 65846.67 13702.90
High indicators 239 27921.52 25860.36 4644.0 65000.00 12629.15

Outsourcing group n Mean Median Min Max Standard dev.
Not outsourced 6924 575.41 539.73 42.97 1423.42 287.17
Outsourced 897 508.80 476.24 51.57 1418.05 274.67
Likely agency 231 538.88 489.87 68.64 1414.75 294.41
High indicators 239 599.91 555.62 99.78 1396.56 271.34

This difference increases to £2951 annually (£63 per week) when we take into account Age, Gender, Education, Ethnicity, Region, and Arrival Time. 36 This analysis shows that all other variables, apart from Age, are in some way relevant to income. On average, and controlling for each of the other variables in the model.

Annually:

  • Men earn £7028 more than women.
  • People who have a degree earn £8195 more than people without a degree.
  • Workers in all non-London regions earn less than workers in London
    • East Midlands: -£5770
    • East of England: -£4074
    • North East: -£4850
    • North West: -£4476
    • Northern Ireland: -£6546
    • Scotland: -£5466
    • South East: -£3406
    • Wales: -£5366
    • West Midlands: -£5002
    • Yorkshire and the Humber: -£5524
  • People who arrived in the UK within the last year earn £6136 less than people born in the UK
  • People who arrived in the UK within the last 3 years earn £2392 less than people born in the UK
  • People who arrived in the UK within the last 5 years earn £2031 less than people born in the UK
  • People who arrived within the last 30 years earn £3501 more than people born in the UK.

Weekly37:

  • Men earn £151 more than women.
  • People who have a degree earn £176 more than people without a degree.
  • Workers in all non-London regions earn less than workers in London
    • East Midlands: -£124
    • East of England: -£88
    • North East: -£104
    • North West: -£96
    • Northern Ireland: -£141
    • Scotland: -£117
    • South East: -£73
    • Wales: -£115
    • West Midlands: -£107
    • Yorkshire and the Humber: -£119
  • People who arrived in the UK within the last year earn £132 less than people born in the UK
  • People who arrived in the UK within the last 3 years earn £51 less than people born in the UK
  • People who arrived in the UK within the last 5 years earn £44 less than people born in the UK
  • People who arrived within the last 30 years earn £75 more than people born in the UK.

4.1 Gender pay gap

#gender-pay-gap
  • On average within our sample, male workers earn £6400 more than female workers per year; but further exploration of how pay relates to gender for outsourced workers suggests that this gender pay gap doesn’t differ in a statistically significant way depending on whether workers are outsourced or not

  • For female outsourced workers, this suggests that being an outsourced worker neither exacerbates nor diminishes the gender pay gap they face compared to male workers. Check what this controls for

4.1.1 Outsourcing status

outsourcing_status Gender n mean median min max stdev
Not outsourced Female 4321 23750.33 22199.18 2234.057 66249.37 12612.378
Not outsourced Male 3576 30197.26 28000.00 2400.000 66000.00 13346.555
Not outsourced Other 11 28154.06 29713.06 10800.000 50000.00 15199.138
Not outsourced Prefer not to say 57 28650.68 25260.52 13684.996 64000.00 17065.444
Outsourced Female 666 20318.32 19600.50 2400.000 65000.00 11603.484
Outsourced Male 863 27983.89 26000.00 3600.000 66085.73 13040.672
Outsourced Other 2 16230.00 21011.55 8460.000 24000.00 9886.687
Outsourced Prefer not to say 7 21245.30 28425.08 11635.714 42000.50 16565.978

outsourcing_status Gender n mean median min max stdev
Not outsourced Female 4321 510.2894 476.9621 48.00000 1423.4057 270.9842
Not outsourced Male 3576 648.8054 601.5961 51.56538 1418.0479 286.7584
Not outsourced Other 11 604.9061 638.4022 232.04420 1074.2787 326.5622
Not outsourced Prefer not to say 57 615.5762 542.7368 294.03000 1375.0767 366.6609
Outsourced Female 666 436.5508 421.1280 51.56538 1396.5623 249.3075
Outsourced Male 863 601.2499 558.6249 77.34807 1419.8899 280.1863
Outsourced Other 2 348.7109 451.4451 181.76796 515.6538 212.4211
Outsourced Prefer not to say 7 456.4674 610.7292 250.00000 902.4048 355.9295

Annual38:

Exploring the gender pay gap by outsourcing status indicates that the pay gap does not differ depending on whether workers are outsourced our not. For non-outsourced workers, females are paid £5800.82 less than males. For outsourced workers, females are paid £6399.5 less than males. The difference between non-outsourced and outsourced workers is not significant.

Weekly39:

Exploring the gender pay gap by outsourcing status indicates that the pay gap does not differ depending on whether workers are outsourced our not. For non-outsourced workers, females are paid £124.63 less than males. For outsourced workers, females are paid £137.5 less than males. The difference between non-outsourced and outsourced workers is not significant.

The gender by outsourcing status is also not relevant for whether a worker is low income (i.e. non-sig relationship with income_group).

4.1.2 Outsourcing group

Annual data files40:

Weekly41:

The gender by outsourcing group is also not relevant for whether a worker is low income (i.e. non-sig relationship with income_group).

#gender-income-group
  • In particular, people are more likely to be in our low-paid outsourced group if they are female, or older workers .

Income group42

A person is more likely to be in the low income group if they are:

  • Older
  • Female
  • Don’t have a degree (or don’t know if they have a degree?)
  • Are outsourced
  • Arrived in the UK in the last year

And less likely if they are:

  • Younger
  • Male
  • Have a degree
  • Live in the North West or Wales (compared to London)
  • Arrived in the UK in last 30 years
#gender-by-pay-split

Is there already a basic low / high pay split for gender? I know you talk about women being more likely to be in the low-paid group, but again not sure if there is just a basic “women make up x% of low pay group and x% of not low pay group”?

60.34% of outsourced workers in the low pay group were female, compared to 35.85% of outsourced workers in the not low pay group. This difference is statistically significant; women are more likely to be in the low income group. This pattern is the same for non outsourced workers, and there is no interaction effect; irrespective of outsourcing status, women are more likely to be low paid, and irrespective of gender, outsourced people are more likely to be low paid.

#pay-gap-sector
  • Overall, we find that workers in administrative and support service activities – one of the dominant sectors for outsourced workers in this research – are more likely to be lower-paid than non-outsourced workers in the same sector. The same is true for outsourced water supply (full name; sewerage, waste etc.) workers – another prominent outsourcing sector – information and communication, transportation and storage, and education workers, amongst others. In contrast, we find outsourced workers in financial and insurance activities, for example, appear to be slightly higher paid on average than their non-outsourced counterparts; however, this is one of the few sectors in which this appears to be the case.to be confirmed

I don’t quite understand the chart below the above chart in the file, would you be able to explain it – thanks! Is this the best chart to use, above? Does this need to control for anything else to show us the most accurate analysis of pay by sector for outsourced and non outsourced, or are we confident that this is showing us something notable about sector and pay?

4.2 Sectors/occupations

4.2.1 Sector and occupation hierarchy

The data from Opinium has four variables relating to sectors/occupations. These are

  • SectorName
  • Majorgroupcode
  • MajorsubgroupOccupation
  • UnitOccupation

SOC 2020 has nine major groups, 26 sub-major groups, 104 minor groups and 412 unit groups. The variables we have appear to map in the following way:

  • Majorgroupcode = the 9 ‘major groups’
  • MajorsubgroupOccupation = the 26 ‘sub-major’ groups
  • UnitOccupation = the 104 ‘minor groups’

This last pairing is the point of confusion. The ‘UnitOccupation’ wording came from Opinium and these categories match the coding index where they are confusingly referred to as ‘unit groups’ even though they are the minor groups.

There is no variable in our data that relates to the most disaggregated category, the 412 ‘unit groups’.

The unique values of each variable are shown in each section below.

4.2.1.1 SectorName

SectorName_labelled
FINANCIAL AND INSURANCE ACTIVITIES
WHOLESALE AND RETAIL TRADE; REPAIR OF MOTOR VEHICLES AND MOTORCYCLES
TRANSPORTATION AND STORAGE
MANUFACTURING
INFORMATION AND COMMUNICATION
ELECTRICITY, GAS, STEAM AND AIR CONDITIONING SUPPLY
CONSTRUCTION
PUBLIC ADMINISTRATION AND DEFENCE; COMPULSORY SOCIAL SECURITY
PROFESSIONAL, SCIENTIFIC AND TECHNICAL ACTIVITIES
WATER SUPPLY; SEWERAGE, WASTE MANAGEMENT AND REMEDIATION ACTIVITIES
HUMAN HEALTH AND SOCIAL WORK ACTIVITIES
EDUCATION
OTHER SERVICE ACTIVITIES
ADMINISTRATIVE AND SUPPORT SERVICE ACTIVITIES
ACCOMMODATION AND FOOD SERVICE ACTIVITIES
AGRICULTURE, FORESTRY AND FISHING
MINING AND QUARRYING
ARTS, ENTERTAINMENT AND RECREATION
REAL ESTATE ACTIVITIES
ACTIVITIES OF HOUSEHOLDS AS EMPLOYERS; UNDIFFERENTIATED GOODS-AND SERVICES-PRODUCING ACTIVITIES OF HOUSEHOLDS FOR OWN US
ACTIVITIES OF EXTRATERRITORIAL ORGANISATIONS AND BODIES
Not found

4.2.1.2 Majorgroupcode

These are the 9 major groups according to SOC

Majorgroupcode_labelled
ADMINISTRATIVE AND SECRETARIAL OCCUPATIONS
MANAGERS, DIRECTORS AND SENIOR OFFICIALS
ELEMENTARY OCCUPATIONS
SALES AND CUSTOMER SERVICE OCCUPATIONS
ASSOCIATE PROFESSIONAL OCCUPATIONS
SKILLED TRADES OCCUPATIONS
PROFESSIONAL OCCUPATIONS
PROCESS, PLANT AND MACHINE OPERATIVES
CARING, LEISURE AND OTHER SERVICE OCCUPATIONS

4.2.1.3 MajorsubgroupOccupation

These are the 26 ‘sub-major’ groups

MajorsubgroupOccupation_labelled
ADMINISTRATIVE OCCUPATIONS
CORPORATE MANAGERS AND DIRECTORS
ELEMENTARY ADMINISTRATION AND SERVICE OCCUPATIONS
CUSTOMER SERVICE OCCUPATIONS
BUSINESS AND PUBLIC SERVICE ASSOCIATE PROFESSIONALS
SKILLED CONSTRUCTION AND BUILDING TRADES
SCIENCE, RESEARCH, ENGINEERING AND TECHNOLOGY PROFESSIONALS
BUSINESS, MEDIA AND PUBLIC SERVICE PROFESSIONALS
PROCESS, PLANT AND MACHINE OPERATIVES
CARING PERSONAL SERVICE OCCUPATIONS
TRANSPORT AND MOBILE MACHINE DRIVERS AND OPERATIVES
SKILLED METAL, ELECTRICAL AND ELECTRONIC TRADES
CULTURE, MEDIA AND SPORTS OCCUPATIONS
SALES OCCUPATIONS
LEISURE, TRAVEL AND RELATED PERSONAL SERVICE OCCUPATIONS
SECRETARIAL AND RELATED OCCUPATIONS
HEALTH AND SOCIAL CARE ASSOCIATE PROFESSIONALS
HEALTH PROFESSIONALS
SKILLED AGRICULTURAL AND RELATED TRADES
TEACHING AND OTHER EDUCATIONAL PROFESSIONALS
SCIENCE, ENGINEERING AND TECHNOLOGY ASSOCIATE PROFESSIONALS
PROTECTIVE SERVICE OCCUPATIONS
OTHER MANAGERS AND PROPRIETORS
ELEMENTARY TRADES AND RELATED OCCUPATIONS
TEXTILES, PRINTING AND OTHER SKILLED TRADES
COMMUNITY AND CIVIL ENFORCEMENT OCCUPATIONS

4.2.1.4 UnitOccupation

These are indeed the 104 ‘minor groups’.

UnitOccupation_labelled
Administrative Occupations: Finance
Managers and Directors in Retail and Wholesale
Functional Managers and Directors
Elementary Storage Occupations
Customer Service Occupations
Business Associate Professionals
Construction and Building Trades Supervisors
Information Technology Professionals
Legal Professionals
Production, Factory and Assembly Supervisors
Administrative Occupations: Government and Related Organisations
Caring Personal Services
Mobile Machine Drivers and Operatives
Quality and Regulatory Professionals
Metal Forming, Welding and Related Trades
Artistic, Literary and Media Occupations
Metal Machining, Fitting and Instrument Making Trades
Sales Assistants and Retail Cashiers
Other Administrative Occupations
Production Managers and Directors
Regulatory Associate Professionals
Public Services Associate Professionals
Animal Care and Control Services
Administrative Occupations: Office Managers and Supervisors
Engineering Professionals
Shopkeepers and Sales Supervisors
Elementary Security Occupations
Other Elementary Services Occupations
Legal Associate Professionals
Architects, Chartered Architectural Technologists, Planning Officers, Surveyors and Construction Professionals
Housekeeping and Related Services
Secretarial and Related Occupations
Welfare and Housing Associate Professionals
Sales, Marketing and Related Associate Professionals
Nursing Professionals
NA
Natural and Social Science Professionals
Agricultural and Related Trades
Elementary Administration Occupations
Process Operatives
Other Educational Professionals
Road Transport Drivers
Information Technology Technicians
Business and Financial Project Management Professionals
Finance Professionals
Therapy Professionals
Electrical and Electronic Trades
Teaching Professionals
HR, Training and Other Vocational Associate Guidance Professionals
Construction Operatives
Protective Service Occupations
Sales Related Occupations
Leisure and Travel Services
Chief Executives and Senior Officials
Teaching and Childcare Support Occupations
Managers and Proprietors in Health and Care Services
Managers and Proprietors in Other Services
Managers in Logistics, Warehousing and Transport
Other Health Professionals
Elementary Construction Occupations
Business, Research and Administrative Professionals
Veterinary nurses
Research and Development (R&D) and Other Research Professionals
Assemblers and Routine Operatives
Welfare Professionals
Science, Engineering and Production Technicians
Finance Associate Professionals
Plant and Machine Operatives
Elementary Cleaning Occupations
Food Preparation and Hospitality Trades
Construction and Building Trades
Senior Officers in Protective Services
Managers and Proprietors in Hospitality and Leisure Services
Other Drivers and Transport Operatives
Health Associate Professionals
Health and Social Services Managers and Directors
Skilled Metal, Electrical and Electronic Trades Supervisors
Administrative Occupations: Records
Sports and Fitness Occupations
Medical Practitioners
Media Professionals
Web and Multimedia Design Professionals
Transport Associate Professionals
Conservation and Environment Professionals
Vehicle Trades
Elementary Process Plant Occupations
Teaching and Childcare Associate Professionals
Cleaning and Housekeeping Managers and Supervisors
Librarians and Related Professionals
Customer Service Supervisors
Elementary Sales Occupations
Veterinarians
Hairdressers and Related Services
Printing Trades
Building Finishing Trades
Managers and Proprietors in Agriculture Related Services
Other Skilled Trades
Directors in Logistics, Warehousing and Transport
Metal Working Machine Operatives
Community and Civil Enforcement Occupations
Design Occupations
Elementary Agricultural Occupations
Textiles and Garments Trades
CAD, Drawing and Architectural Technicians

4.2.2 Sectoral pay differences43

4.2.3 Occupations44

Here we look at Major subgroup occupations within sectors. We only consider the down to ‘Other services’, as the remaining sectors have small n for outsourced group. Note you can find larger images for these plots in outputs/figures/occupation_pay_plots.

The figures indicate there is variation between occupations within sectors in terms of whether outsourced people are paid less or more than non-outsourced workers.

4.2.4 Weekly pay penalty in occupations within sectors

Note I only consider unit occupations where the the minimum n is >= 10.

Many instances where outsourced workers within a unit occupation are paid less than their non-outsourced counterparts:45

Weekly pay penalty for unit occupations within sectors
sector_name_labelled unit_occupation_labelled pay_penalty wtd_avg_income_not_outsourced wtd_avg_income_outsourced n_not_outsourced n_outsourced
Manufacturing Functional Managers And Directors -293.934661 796.9414 503.0067 35 14
Wholesale And Retail Trade; Repair Of Motor Vehicles And Motorcycles Functional Managers And Directors -214.846399 750.7923 535.9459 50 12
Information And Communication Information Technology Technicians -195.211801 783.5755 588.3637 16 11
Administrative And Support Service Activities Customer Service Occupations -120.175519 437.9813 317.8058 11 10
Administrative And Support Service Activities Elementary Cleaning Occupations -108.761529 324.9068 216.1453 15 16
Information And Communication Information Technology Professionals -94.301622 935.2414 840.9397 79 26
Education Teaching Professionals -83.084349 674.6332 591.5488 283 40
Information And Communication Functional Managers And Directors -68.796060 933.1020 864.3060 28 11
Human Health And Social Work Activities Nursing Professionals -61.552329 670.7051 609.1528 177 35
Accommodation And Food Service Activities Other Elementary Services Occupations -59.551254 336.4167 276.8655 94 32
Education Teaching And Childcare Support Occupations -46.708093 335.0658 288.3577 137 26
Transportation And Storage Road Transport Drivers -32.418643 606.6916 574.2729 71 19
Human Health And Social Work Activities Caring Personal Services -28.207324 424.4531 396.2457 301 81
Human Health And Social Work Activities Other Health Professionals -27.517406 662.8731 635.3557 50 13
Wholesale And Retail Trade; Repair Of Motor Vehicles And Motorcycles Road Transport Drivers -23.475416 524.6526 501.1772 37 11
Wholesale And Retail Trade; Repair Of Motor Vehicles And Motorcycles Shopkeepers And Sales Supervisors -20.296337 454.6491 434.3528 55 20
Wholesale And Retail Trade; Repair Of Motor Vehicles And Motorcycles Elementary Storage Occupations -8.014079 453.8159 445.8018 53 14

4.2.5 Weekly pay penalty in occupations across all sectors

Note I only consider unit occupations where the the minimum n is >= 10.

Looking at occupations across all sectors, there are many occupations where outsourced workers within a unit occupation are paid less than their non-outsourced counterparts:46

Weekly pay penalty for unit occupations across all sectors
unit_occupation_labelled pay_penalty wtd_avg_income_not_outsourced wtd_avg_income_outsourced n_not_outsourced n_outsourced
Protective Service Occupations -186.11591 798.1466 612.0307 87 11
Administrative Occupations: Government And Related Organisations -173.25254 660.8699 487.6173 150 11
Information Technology Technicians -172.50995 748.9725 576.4626 90 27
Elementary Administration Occupations -125.57767 477.9704 352.3927 34 11
Functional Managers And Directors -89.04844 820.2879 731.2395 385 88
Business, Research And Administrative Professionals -88.82911 845.0837 756.2546 101 18
Teaching Professionals -82.42140 675.1625 592.7411 293 41
Nursing Professionals -70.58157 673.2751 602.6935 180 36
Sales, Marketing And Related Associate Professionals -69.20211 684.0741 614.8720 155 20
Business Associate Professionals -65.93246 685.3387 619.4062 125 21
Finance Professionals -64.48880 805.6680 741.1792 110 20
Information Technology Professionals -62.39887 887.5669 825.1680 231 50
Finance Associate Professionals -54.41873 726.6762 672.2575 55 12
Teaching And Childcare Support Occupations -53.27575 348.2522 294.9765 156 28
Shopkeepers And Sales Supervisors -46.32116 479.0732 432.7521 87 27
Science, Engineering And Production Technicians -39.51294 644.4197 604.9067 76 11
Secretarial And Related Occupations -39.21838 477.0919 437.8735 146 17
Welfare And Housing Associate Professionals -38.62785 525.0780 486.4501 84 10
Other Elementary Services Occupations -38.03264 307.3713 269.3387 144 39
Customer Service Occupations -35.31753 493.9996 458.6820 163 29
Other Health Professionals -32.72395 671.4596 638.7357 62 17
Road Transport Drivers -26.76629 569.7437 542.9774 154 41
Caring Personal Services -25.12189 422.0606 396.9387 332 87
Elementary Cleaning Occupations -18.85941 282.8777 264.0183 113 60

4.3 London has a disproportionate share of the UK’s outsourced workers, followed by the East and West Midlands

#regions
  • In London, around 25% of workers are outsourced – the highest proportion of any region in the UK. London is followed by the East Midlands (19%) and West Midlands (18%) in the share of workers in the region who are outsourced, with the East of England being the region with the lowest share of outsourced workers as part of the total employed workforce, at 13%.

  • Possible addition: Should this include some comment on WHY we think this might be the case? Should we look at sectoral splits in London, compared to everywhere else, to see whether there are significant sector differences that might explain this trend?

The plot below shows the proportion of workers within each region who are outsourced.47

Below we map the workforce composition in each region. The first map emphasises that London has the highest concentration of outsourced workers (25%).

2024-06-21T14:45:45.707634 image/svg+xml Matplotlib v3.8.2, https://matplotlib.org/

The second map excludes London so that is easier to see how the remaining regions compare. After London, the regions with the highest proportion of outsourced workers are:

  1. East Midlands (19%)
  2. West Midlands (18%)
  3. Wales (18%)
  4. North West (17%)
  5. Northern Ireland (16%)

2024-06-21T14:45:46.107661 image/svg+xml Matplotlib v3.8.2, https://matplotlib.org/

We can also explore how the the entire UK workforce is distributed across the country.48 The table and map below show the percentage of outsourced workers in each region as a proportion of the total UK workforce. They show where the UK’s outsourced workforce is concentrated. The regions with the highest share of the UK’s outsourced workforce are:

  1. London (21%)
  2. North West (11%)
  3. South East (11%)
  4. West Midlands (9%)
  5. East Midlands (8%)
Region Frequency Sum Percentage
London 357.35 1708.36 20.92
North West 189.39 1708.36 11.09
South East 188.47 1708.36 11.03
West Midlands 161.49 1708.36 9.45
East Midlands 140.50 1708.36 8.22
Scotland 125.82 1708.36 7.37
East of England 125.49 1708.36 7.35
South West 120.50 1708.36 7.05
Yorkshire and the Humber 119.46 1708.36 6.99
Wales 83.25 1708.36 4.87
North East 53.06 1708.36 3.11
Northern Ireland 43.56 1708.36 2.55

2024-06-21T14:45:46.548602 image/svg+xml Matplotlib v3.8.2, https://matplotlib.org/

Footnotes

  1. outputs/data/total_outsourced.csv↩︎

  2. outputs/data/total_outsourced_2.csv↩︎

  3. outputs/data/number_of_groups.csv↩︎

  4. outputs/data/status_by_ethnicity.csv↩︎

  5. outputs/data/status_by_ethnicity_full.csv↩︎

  6. outputs/data/status_by_ethnicity_income_group.csv↩︎

  7. outputs/data/status_by_ethnicity_full_income_group.csv↩︎

  8. outputs/data/group_by_ethnicity.csv↩︎

  9. outputs/data/group_by_ethnicity_full.csv↩︎

  10. outputs/data/group_by_ethnicity_income_group.csv↩︎

  11. outputs/data/group_by_ethnicity_full_income_group.csv↩︎

  12. outputs/data/status_by_gender.csv↩︎

  13. outputs/data/status_by_gender_income_group.csv↩︎

  14. outputs/data/group_by_gender.csv↩︎

  15. outputs/data/group_by_gender_income_group.csv↩︎

  16. outputs/data/ethnicity_stats_1.csv & outputs/data/ethnicity_binary_o-status_inferential_tab.csv↩︎

  17. outputs/data/ethnicity_binary_income_group.csv↩︎

  18. outputs/data/ethnicity_model_inferential.csv↩︎

  19. outputs/data/ethnicity_model_inferential_2.csv↩︎

  20. outputs/data/ethnicity_ogroup_inferential_tab.csv↩︎

  21. outputs/data/ethnicity_ogroup_inferential_tab_2.csv↩︎

  22. outputs/data/arrival_in_UK_stats.csv↩︎

  23. outputs/data/bornuk_ostatus_inferential_tab.csv↩︎

  24. outputs/data/bornUK_binary_contrasts.csv↩︎

  25. outputs/data/region_stats_2.csv↩︎

  26. outputs/data/region_stats_2.csv↩︎

  27. outputs/data/region_stats_3.csv↩︎

  28. outputs/data/sector_summary_3.csv↩︎

  29. outputs/data/sector_summary_3.csv↩︎

  30. ../outputs/data/gender_inferential_tab.csv↩︎

  31. outputs/data/sector_summary_3.csv↩︎

  32. outputs/data/income_stats_o-group.csv & outputs/data/model_income_by_o-group.csv↩︎

  33. outputs/data/weekly_income_stats_o-status.csv & outputs/data/model_income_by_o-status_weekly.csv↩︎

  34. outputs/data/income_stats_o-group.csv & outputs/data/model_income_by_o-group.csv↩︎

  35. outputs/data/weekly_income_stats_o-group.csv & outputs/data/model_income_by_o-group_weekly.csv↩︎

  36. outputs/data/model_2_income_by_o-status.csv↩︎

  37. outputs/data/model_2_income_by_o-status_weekly.csv↩︎

  38. outputs/data/o-status_gender_gap.csv & outputs/data/mod_o-status_gender.csv↩︎

  39. outputs/data/o-status_gender_gap_weekly.csv & outputs/data/mod_o-status_gender_weekly.csv↩︎

  40. outputs/data/o-group_gender_gap.csv.csv & outputs/data/mod_o-group_gender.csv↩︎

  41. outputs/data/o-group_gender_gap_weekly.csv & outputs/data/mod_o-group_gender_weekly.csv↩︎

  42. ../outputs/data/income_group_outsourcing.csv↩︎

  43. outputs/data/sector_summary_pay_weekly.csv↩︎

  44. outputs/data/occupation_in_sector_summary_pay_weekly.csv↩︎

  45. outputs/data/unit_occupation_in_sector_weekly_pay_penalty.csv↩︎

  46. outputs/data/unit_occupation_weekly_pay_penalty.csv↩︎

  47. outputs/data/region_stats_2.csv↩︎

  48. outputs/data/region_stats_3.csv↩︎